| 基于多源数据的旅游者视觉行为模式与感知评估方法 |
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| 引用本文： | 李渊,郭晶,陈一平.基于多源数据的旅游者视觉行为模式与感知评估方法[J].地球信息科学,2022,24(10):2004-2020. |
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| 作者姓名： | 李渊 郭晶 陈一平 |
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| 作者单位： | 1.厦门大学建筑与土木工程学院,厦门 3610052.福建省智慧城市感知与计算重点实验室,厦门 3610053.厦门大学信息学院,厦门 361005 |
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| 基金项目： | 国家自然科学基金项目(42171219);福建省自然科学基金项目(2020J01011) |
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| 摘 要： | 用户生成内容（User Generated Content,UGC）作为感知旅游地物质空间的新型地理大数据,以使用者的视角描绘了旅游地的客观环境,是探索旅游目的地感知的重要途径。然而,传统的旅游研究对旅行摄影照片处理能力有限,深度学习图像语义分割技术的发展,为挖掘旅游者视觉行为模式,探索旅游地环境感知提供了有力支持。本研究提出了整合在线旅行照片大数据与问卷调查小数据的旅游者视觉行为模式与感知评估框架,并将其应用于鼓浪屿案例。首先将744条旅游轨迹,聚类为6类视觉行为模式,并可视化与时空分析;其次基于全卷积网络算法,量化22 507张旅行照片语义,探索不同视觉模式的旅游者关注要素的空间分异;最后通过照片语义与场景感知问卷调查的相关性分析和多重线性回归模型,评估旅游地整体视觉感知满意度,并提出相应的空间优化建议。研究表明：① 鼓浪屿旅游者视觉行为模式聚类为单点游、海岛风光游、环岛游、街巷空间游、遗产建筑游和全岛游6类;② 不同视觉行为模式的旅游者视觉兴趣区存在空间集聚现象,视觉空间转移遵循地理邻近效应;③ 相关性分析与模型结果表明,旅游者偏好空间开敞度较高的区域,感知满意度越低的区域摄影行为越少,是环境提升的重点;④ 出行时间和成本效率最大化、建成环境、心理环境与社会环境是影响旅游者视觉感知的主要因素。本研究延伸了人工智能技术在旅游者视觉感知研究中的应用,为旅游地空间优化提供参考。
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| 关 键 词： | UGC数据 视觉行为模式 感知评估 旅行摄影 轨迹聚类 深度学习 场景语义 鼓浪屿 |
| 收稿时间： | 2021-12-30 |
A New Approach for Tourists' Visual Behavior Patterns and Perception Evaluation based on Multi-source Data |
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| LI Yuan,GUO Jing,CHEN Yiping.A New Approach for Tourists' Visual Behavior Patterns and Perception Evaluation based on Multi-source Data[J].Geo-information Science,2022,24(10):2004-2020. |
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| Authors: | LI Yuan GUO Jing CHEN Yiping |
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| Institution: | 1. School of Architecture and Civil Engineering, Xiamen University, Xiamen 361005, China2. Fujian Key Laboratory on Sensing and Computing for Smart City, Xiamen 361005, China3. School of Informatics, Xiamen University, Xiamen 361005, China |
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| Abstract: | User Generated Content (UGC), as a new type of geographic big data for perceiving the physical space of tourism destination, depicts the objective environment of tourism destination from the perspective of users, which is an important way to explore the perception of tourism destination. However, the traditional tourism research has limited ability to deal with travel photos. The development of deep learning image semantic segmentation technology provides strong support for mining tourists' visual behavior patterns and exploring tourism destination environmental perception. This study proposes a framework for tourists' visual behavior model and perception evaluation, which integrates the big data of online travel photos and small data of questionnaire survey, and applies it to the case of Gulangyu Island. Firstly, 744 tourism trajectories are clustered into six types of visual behavior patterns, and visualized and spatiotemporal analysis is carried out; Secondly, based on the full convolution network algorithm, the semantics of 22 507 travel photos are quantified to explore the spatial differentiation of the elements concerned by tourists with different visual modes; Finally, through the correlation analysis of photo semantics and scene perception questionnaire and the multiple linear regression model, the overall visual perception satisfaction of tourism destination is evaluated, and the corresponding spatial optimization suggestions are put forward. The results show that: (1) the visual behavior patterns of tourists on Gulangyu Island are clustered into six categories: single point tour, island scenery tour, around the island tour, street and lane space tour, heritage building tour, and whole island tour; (2) Tourists with different visual behavior patterns have spatial agglomeration in their visual interest areas, and the transfer of visual space follows the geographical proximity effect; (3) The results of correlation analysis and model show that tourists prefer areas with high spatial openness, and the areas with lower perceived satisfaction have less photography behavior, which is the focus of environmental improvement; (4) Maximizing travel time and cost efficiency, built environment, psychological environment, and social environment are the main factors affecting tourists' visual perception. This study extends the application of artificial intelligence technology in the study of tourists' visual perception, and provides a reference for tourism destination spatial optimization. |
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| Keywords: | UGC data visual pattern perception assessment travel photography trajectory clustering deep learning scene semantics Gulangyu Island |
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